There is considerable interest in developing new energy storage technologies for the electric grid, but economic viability will require that manufacturers provide warranties guaranteeing 15+ years of life.  While there are extensive efforts to make early predictions for the expected life of new storage technologies, we argue here that for purposes of pricing warranties and valuing second life potential—considerations that are crucial to whether the technologies can be commercialized—the full failure probability distribution, not just the expected life, is required.  We use published battery cycle-life data to suggest efficient statistical and machine learning-based testing and analysis strategies that can rapidly estimate and also take advantage of the failure probability distribution.  One approach is a Weibull analysis, which can (a) reduce the number of testing machine-hours required for setting a warranty, (b) quickly determine whether a new technology is better than a baseline technology, and (c) estimate the maximum intensity of testing acceleration that does not change the failure mode.  A second approach is driven by the idea that all measured data — such as capacity or energy as a function of time or cycle number — is valuable and generated by an underlying latent function.  The analysis employs a Gaussian process to find the underlying latent function, together with its uncertainties, which can be used to calculate the failure distribution.